- A
Retraining the model from scratch on the legal dataset
Why wrong: Retraining from scratch is unnecessary and impractical—it would require a much larger dataset and massive compute.
- B
Adapter-based fine-tuning using LoRA
LoRA fine-tunes a small set of adapter parameters, preserving general knowledge while adapting to the specific task with a small dataset.
- C
In-context learning with a few examples in the prompt
Why wrong: In-context learning does not update the model; it relies on examples in the prompt, which may not be sufficient for reliable summarization.
- D
Full fine-tuning of all model parameters
Why wrong: With only 500 examples, full fine-tuning risks catastrophic forgetting and overfitting to the small dataset.
Question 362 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is fine-tuning a large language model for a legal document summarization task. The dataset contains only 500 examples, and the model must not forget its general language capabilities. Which fine-tuning method is most suitable?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Adapter-based fine-tuning using LoRA
LoRA (Low-Rank Adaptation) is an adapter-based fine-tuning method that updates a small number of parameters while keeping the base model frozen. This prevents catastrophic forgetting and works well with small datasets. Full fine-tuning would risk overfitting and losing general capabilities.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retraining the model from scratch on the legal dataset
Why it's wrong here
Retraining from scratch is unnecessary and impractical—it would require a much larger dataset and massive compute.
- ✓
Adapter-based fine-tuning using LoRA
Why this is correct
LoRA fine-tunes a small set of adapter parameters, preserving general knowledge while adapting to the specific task with a small dataset.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
In-context learning with a few examples in the prompt
Why it's wrong here
In-context learning does not update the model; it relies on examples in the prompt, which may not be sufficient for reliable summarization.
- ✗
Full fine-tuning of all model parameters
Why it's wrong here
With only 500 examples, full fine-tuning risks catastrophic forgetting and overfitting to the small dataset.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Generative AI Concepts and Technologies — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Adapter-based fine-tuning using LoRA — LoRA (Low-Rank Adaptation) is an adapter-based fine-tuning method that updates a small number of parameters while keeping the base model frozen. This prevents catastrophic forgetting and works well with small datasets. Full fine-tuning would risk overfitting and losing general capabilities.
What should I do if I get this Generative AI Leader question wrong?
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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